Model Reconstruction Model Reconstruction, also known as fine-tuning, aims to retrain the infected DL model with a small amount of benign data to alleviate the effect of backdoor attack [1]}, [2]}. This strategy is based on the theory of the catastrophic forgetting property of the deep neural networks [3]}. Since in FL, each client owns a specific quantity of benign data, thus model reconstruction matches the FedGAN setting. In order to simulate the scenarios in the reality, we reconstruct the model with a dataset containing 500 real images per class after receiving the trained generator model from the FedGAN server. In this experiment, we fine-tune the innocuous samples with 200 epochs and examine their performance.
